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Assessing tumor extent on contrast-enhanced spectral mammography versus full-field digital mammography and ultrasound.

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OBJECTIVES To compare breast cancer size measurements on full-field digital mammography (FFDM), contrast-enhanced spectral mammography (CEDM), and ultrasound (US), with histologic tumor size used as the reference standard. Material and… Click to show full abstract

OBJECTIVES To compare breast cancer size measurements on full-field digital mammography (FFDM), contrast-enhanced spectral mammography (CEDM), and ultrasound (US), with histologic tumor size used as the reference standard. Material and methods The HIPAA complaint, IRB approved study comprised 88 women with newly diagnosed breast cancer who underwent FFDM and CEDM;74 also had US. Breast density, histologic subtype, and maximum tumor measurements were recorded. RESULTS Pearson correlation coefficients for FFDM, US, and CEDM vs histopathology were 0.598, 0.639, and 0.859, respectively (P<0.001). The following correlation coefficients were calculated for dense breasts (n=48): histopathology vs FFDM (0.555), US (0.633), and CEDM (0.843) (P<0.001); for nondense breasts (n=40), they were FFDM (0.618), US (0.512), and CEDM (0.885) (P<0.001). For size difference, the mean (SD) for histopathology vs FFDM, US, and CEDM was -1.3 (11.9) mm, -2.8 (11.1) mm, and 2.9 (9.5) mm, respectively. Limits of agreement were -24.8 to 22.0mm, -24.5 to 18.8mm, and -15.6 to 21.4mm, respectively. CONCLUSIONS In patients with biopsy-proven malignancy, size measurements correlated well with histopathologic size, and were higher on CEDM than those for FFDM and US in patients with dense or nondense breasts. The added value of CEDM as a supplement to FFDM in determining tumor size, however, was greater in patients with dense breasts. CEDM may be a promising alternative preoperative measurement tool for breast cancer patients with dense breasts and/or limited access or contraindications to MRI.

Keywords: ffdm; histopathology; cedm; size; mammography; tumor

Journal Title: Clinical imaging
Year Published: 2017

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